How to Use the D-ID MCP in Pydantic AI

Q: Does Pydantic AI validate the response of D-ID tool calls?

Yes. Every tool call, such as getcredits, has its return payload validated against strict internal schemas. If the API structure changes, your agent raises a validation error immediately.

Q: Can I use Pydantic AI to build custom D-ID video workflows?

Absolutely. You can chain tools like uploadimage and createtalk together. The framework ensures that the output of the upload step perfectly matches the input requirements of the video creation step.

Q: How do we monitor video generation progress?

Your agent polls getclip or gettalk to check the status. The returned JSON is parsed into a structured model, letting your agent know exactly when the video URL is ready to use.

Run type-safe video pipelines with D-ID and Pydantic AI for guaranteed runtime schemas.

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Connect D-ID MCP to Pydantic AI

Create your Vinkius account to connect D-ID to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup D-ID with Pydantic AI

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Type-safe avatar creation using Pydantic AI

Stop worrying about malformed API payloads. Your agent calls `create_talk` to spin up talking avatars, validating every parameter against strict Pydantic schemas. If the video service returns unexpected data, the framework catches it instantly. You get clean, validated outputs when checking progress with `get_talk`.

Strict validation on this MCP Server

This MCP Server forces strict validation on your media assets to prevent silent failures. The agent uses `list_talks` to fetch your history, parsing the timestamps and statuses into typed Python models. When you need to remove old content, `delete_talk` executes with type-safe ID validation. This prevents your code from sending malformed strings to the endpoint.

Structured asset uploading

Uploading custom presenters requires strict data handling. Your agent calls `upload_image` to register a face, ensuring the returned URL matches your expected string format. You can also fetch pre-built options using `list_presenters`. The tool returns structured records that your agent can safely pass to `create_clip` without risking runtime crashes.

Setup guide

Set up D-ID MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "d-id-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to D-ID tools.",
)

result = await agent.run("List recent D-ID transactions")
print(result.output)

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Common questions about D-ID MCP in Pydantic AI

Install the MCP extra, then import MCPToolset pointing to your Vinkius HTTP URL. Pass this toolset directly into your Agent's toolsets argument.

Yes. Every tool call, such as `get_credits`, has its return payload validated against strict internal schemas. If the API structure changes, your agent raises a validation error immediately.

Absolutely. You can chain tools like `upload_image` and `create_talk` together. The framework ensures that the output of the upload step perfectly matches the input requirements of the video creation step.

Your agent polls `get_clip` or `get_talk` to check the status. The returned JSON is parsed into a structured model, letting your agent know exactly when the video URL is ready to use.

The Vinkius integration uses isolated V8 environments to execute calls. Your face data and images are transmitted via TLS directly to the API, bypassing any shared storage to keep your visual assets private.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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